根据蛋白质互作网络预测前列腺癌相关蛋白质的细致功能

对于功能部分已知的前列腺癌相关蛋白质,提出了一种基于Gent Ontology的功能特异的子网构建方法来细化其功能注释。结果显示该方法能够以很高的精确率为前列腺癌相关蛋白质预测更为精细的功能。预测的相关蛋白质的功能对于指导实验研究前列腺癌的分子机制具有重要的价值。     

口腔癌相关蛋白质的细致功能预测

口腔鳞状细胞癌（oral squamous cell carcinoma,OSCC）是人类最常见的恶性肿瘤之一。本文结合Gene Ontology,功能先验知识,通过构建功能特异的蛋白质互相作用子网来预测口腔癌相关蛋白质的功能。我们以很高的精确率为已知部分功能的口腔癌相关蛋白质预测了更为精细的功能,这对于指导实验研究口腔癌的分子机制具有重要的价值。     

蛋白质功能预测方法概述

蛋白质是生物体内最必需也是最通用的大分子,对它们功能的认识对于科学领域和农业领域的发展有着至关重要的作用。随着后基因组时代的发展,NCBI数据库中迅速涌现出大量不明结构与功能的蛋白质序列,这些蛋白质序列甚至一跃成了研究的热点。近几十年来蛋白质功能预测的方法不断被完善。由最初的仅基于蛋白质序列或3D结构信息的方法衍生出更多的基于序列相似性、基于结构基序、基于相互作用网络等新方法,这些新型方法采用新的算法、新的研究思路和技术手段,力求得到准确性与普遍性并存,能够被广泛应用的蛋白质功能预测方法。本文综述了近年来蛋白质功能预测的方法,并将这些研究方法分类归纳,各自阐明了每类方法的优缺点。     

大规模蛋白质功能预测方法的进展

全基因组测序的快速发展在获得大量序列信息的同时也迫切需要获取功能信息,用生物信息学方法进行大规模蛋白质功能预测在这种需求中获得发展。这些预测方法从基于序列同源性发展到基于genomic-context获得功能相关蛋白质对。基于genomic-context的方法具体有基因融合、染色体邻近、相似系统发生谱等。由于各种方法的偏向性,最新的趋势是整合多种方法的数据,组成蛋白质相互作用网络,通过分析网络的结构进行蛋白质功能预测。     

基于相互作用的蛋白质功能预测

蛋白质功能预测是后基因时代研究的热点问题。基于相互作用的蛋白质功能预测方法目前应用比较广泛,但是当"伙伴蛋白质"(interacting partners)数目k较小时,其预测准确率不高。从蛋白质相互作用网络入手,结合"小世界网络"特性,有效解决了k较小时预测准确率不高的问题。对酵母(Saccharomyces cerevisiae)蛋白质的相互作用网络进行预测,当k≤4时其预测准确率比相同条件下的GO(global optimization)方法有一定提高。实验结果表明:该方法能够有效的应用于伙伴蛋白质数目较小时的蛋白质功能预测。     

生物信息学方法预测蛋白质相互作用网络中的功能模块

蛋白质相互作用是大多数生命过程的基础。随着高通量实验技术和计算机预测方法的发展,在各种生物中已获得了数目十分庞大的蛋白质相互作用数据,如何从中提取出具有生物学意义的数据是一项艰巨的挑战。从蛋白质相互作用数据出发获得相互作用网络进而预测出其中的功能模块,对于蛋白质功能预测、揭示各种生化反应过程的分子机理都有着极大的帮助。我们分类概括了用生物信息学预测蛋白质相互作用功能模块的方法,以及对这些方法的评价,并介绍了蛋白质相互作用网络比较的一些方法。     

结合蛋白质互作与基因表达谱信息大范围预测蛋白质的精细功能

GESTs(gene expression similarity and taxonomy similarity)是结合基因表达相似性和基因功能分类体系Gene Ontology (GO)中的功能概念相似性测度进行功能预测的新方法. 将此预测算法推广应用于蛋白质互相作用数据, 并提出了几种在蛋白质互作网络中为功能待测蛋白质筛选邻居的方法. 与已有的其它蛋白质功能预测方法不同, 新方法在学习过程中自动地从功能分类体系中的各个功能类中选择最合适的尽可能具体细致的功能类, 利用注释于其相近功能类中的互作邻居蛋白质支持对此具体功能类的预测. 使用MIPS提供的酵母蛋白质互作信息与一套基因表达谱数据, 利用特别针对GO体系结构层次特点设计的3种测度, 评价对GO知识体系中的生物过程分支进行蛋白质功能预测的效果. 结果显示, 利用文中的方法, 可以大范围预测蛋白质的精细功能. 此外, 还利用此方法对2004年底Gene Ontology上未知功能的蛋白质进行预测, 其中部分预测结果在2006年4月发布的SGD注释数据中已经得到了证实.     

基于蛋白质网络功能模块的蛋白质功能预测

在破译了基因序列的后基因组时代,随着系统生物学实验的快速发展,产生了大量的蛋白质相互作用数据,利用这些数据寻找功能模块及预测蛋白质功能在功能基因组研究中具有重要意义.打破了传统的基于蛋白质间相似度的聚类模式,直接从蛋白质功能团的角度出发,考虑功能团间的一阶和二阶相互作用,提出了模块化聚类方法(MCM),对实验数据进行聚类分析,来预测模块内未知蛋白质的功能.通过超几何分布P值法和增、删、改相互作用的方法对聚类结果进行预测能力分析和稳定性分析.结果表明,模块化聚类方法具有较高的预测准确度和覆盖率,有很好的容错性和稳定性.此外,模块化聚类分析得到了一些具有高预测准确度的未知蛋白质的预测结果,将会对生物实验有指导意义,其算法对其他具有相似结构的网络也具有普遍意义.     

蛋白质二级结构预测方法初探

蛋白质二级结构的预测是生物信息学中一个重要的研究课题,在对蛋白质组的研究中也是最具难度的一个问题。进行二级结构预测对于理解蛋白质结构与功能的关系,以及分子设计、生物制药等领域都有重要的现实意义。同时也是一级结构与三级结构所联系的媒介,也为三级结构的研究打下基础。虽然目前预测的方法有几十种,但准确率最高的也只有70%多,本文对于目前方法进行分析,希望从中得到更加准确的方法。     

基于贝叶斯网络和相互作用可信度的蛋白质功能预测方法

蛋白质功能注释是后基因组时代研究的核心内容之一,基于蛋白质相互作用网络的蛋白质功能预测方法越来越受到研究者们的关注.提出了一种基于贝叶斯网络和蛋白质相互作用可信度的蛋白质功能预测方法.该方法在功能预测过程中为待注释的蛋白质建立贝叶斯网络预测模型,并充分考虑了蛋白质相互作用的可信度问题.在构建的芽殖酵母数据集上的三重交叉验证测试表明,在功能预测过程中考虑蛋白质可信度能够有效地提高功能预测的性能.与现有一些算法相比,该方法能够给出令人满意的预测效果.     

Comparative proteome analysis of breast cancer and normal breast

Breast cancer is a leading cause of death for women. The underlying molecular mechanism is still not well understood. In this study, two-dimensional gel electrophoresis combined with mass spectrometry was used to analyze changes in the proteome of infiltrating ductal carcinoma compared to normal breast tissue. Ten sets of two-dimensional gels per experimental condition were analyzed and more than 500 spots each were detected. This revealed 39 spots for which expression in breast cancer cells were reproducibly altered more than twofold compared to normal controls (p<0.01). These spots represented 25 different proteins after identification using the database search after mass spectrometry, comprising cell defense proteins, enzymes involved in glycolytic energy metabolism and homeostasis, protein folding and structural proteins, proteins involved in cytoskeleton and cell motility, and proteins involved in other functions. In addition, 28 nondifferentially expressed proteins with different functions were also mapped and identified, which might help to establish a two-dimensional gel electrophoresis reference map of human breast cancer. Our study shows that proteomics offers a powerful methodology to detect the proteins that show different expression patterns in breast cancer tissue and may provide an accurate molecular classification. The differentially expressed proteins may be used as potential candidate markers for diagnostic purposes or for determination of tumor sensitivity to therapy. The functional implications of the identified proteins are discussed.     

Gastric cancer biomarkers; A systems biology approach

Gastric cancer is one of the most fatal cancers in the world. Many efforts in recent years have attempted to find effective proteins in gastric cancer. By using a comprehensive list of proteins involved in gastric cancer, scientists were able to retrieve interaction information. The study of protein-protein interaction networks through systems biology based analysis provides appropriate strategies to discover candidate proteins and key biological pathways.In this study, we investigated dominant functional themes and centrality parameters including betweenness as well as the degree of each topological clusters and expressionally active sub-networks in the resulted network. The results of functional analysis on gene sets showed that neurotrophin signaling pathway, cell cycle and nucleotide excision possess the strongest enrichment signals. According to the computed centrality parameters, HNF4A, TAF1 and TP53 manifested as the most significant nodes in the interaction network of the engaged proteins in gastric cancer. This study also demonstrates pathways and proteins that are applicable as diagnostic markers and therapeutic targets for future attempts to overcome gastric cancer.     

Mitochondrial proteomics analysis of tumorigenic and metastatic breast cancer markers

Mitochondria are key organelles in mammary cells responsible for several cellular functions including growth, division, and energy metabolism. In this study, mitochondrial proteins were enriched for proteomics analysis with the state-of-the-art two-dimensional differential gel electrophoresis and matrix-assistant laser desorption ionization-time-of-flight mass spectrometry strategy to compare and identify the mitochondrial protein profiling changes between three breast cell lines with different tumorigenicity and metastasis. The proteomics results demonstrate more than 1,500 protein features were resolved from the equal amount pooled from three purified mitochondrial proteins, and 125 differentially expressed spots were identified by their peptide finger print, in which, 33 identified proteins belonged to mitochondrial proteins. Eighteen out of these 33 identified mitochondrial proteins such as SCaMC-1 have not been reported in breast cancer research to our knowledge. Additionally, mitochondrial protein prohibitin has shown to be differentially distributed in mitochondria and in nucleus for normal breast cells and breast cancer cell lines, respectively. To sum up, our approach to identify the mitochondrial proteins in various stages of breast cancer progression and the identified proteins may be further evaluated as potential breast cancer markers in prognosis and therapy.     

A taxonomy of organ-specific breast cancer metastases based on a protein-protein interaction network

We carried out a systems-level study of the mechanisms underlying organ-specific metastases of breast cancer. We followed a network-based approach using microarray expression data from human breast cancer metastases to select organ-specific proteins that exert a range of functions allowing cell survival and growth in the microenvironment of distant organs. MinerProt, a home-made software application, was used to group organ-specific signatures of brain (1191 genes), bone (1623 genes), liver (977 genes) and lung (254 genes) metastases by function and select the most differentially expressed gene in each function. As a result, we obtained 19 functional representative proteins in brain, 23 in bone, 15 in liver and 9 in lung, with which we constructed four organ-specific protein-protein interaction networks. The network taxonomy included seven proteins that interacted in brain metastasis, which were mainly associated with signal transduction. Proteins related to immune response functions were bone specific, while those involved in proteolysis, signal transduction and hepatic glucose metabolism were found in liver metastasis. No experimental protein-protein interaction was found in lung metastasis; thus, computationally determined interactions were included in this network. Moreover, three of these selected genes (CXCL12, DSC2 and TFDP2) were associated with progression to specific organs when tested in an independent dataset. In conclusion, we present a network-based approach to filter information by selecting key protein functions as metastatic markers or therapeutic targets.     

KLF6 inhibits estrogen receptor-mediated cell growth in breast cancer via a c-Src-mediated pathway

Estrogen receptors play a key role in breast cancer development and progression. Kruppel-like factor 6 (KLF6) is a tumour-suppressing protein. The aim of this study was to identify the role of KLF6 inhibition in estrogen receptor{alpha} (ERα)-elicited breast cancer development. Protein expression levels were examined by western blot analysis and immunoprecipitation was used to analyse interactions between proteins. An MTT assay was used to study cell proliferation. We found that KLF6 mediates cell growth in ERα-positive breast cancer cells through interaction with the c-Src protein. This interaction causes inactivation of the Erk and Akt proteins. These pathways are critical for the proliferation and survival of breast cancer cells. We also established that KLF6 could not mediate cell growth in ERα-negative cells. We conclude that KLF6 can modulate ERα-mediated cell growth in breast cancer cells. The unique role of KLF6 in mediating cell growth in breast cancer cells opens up the possibility of a new therapeutic strategy for treating breast cancer.     

What do women know about breast cancer prophylaxis and a healthy style of life?

AimThe aim of the study was to determine the factors influencing women's knowledge concerning breast cancer prophylaxis and find out the sources of the knowledge.BackgroundIn the Greater Poland region, breast cancer has been the most frequently detected tumour for years. The percentage of breast cancer cases has increased by 31% in the last decade.Materials and methodsThe study encompassed 337 women aged 40–59 who participated in the mammographic examinations. An original research tool was used which assessed the level of knowledge concerning breast cancer prophylaxis, the knowledge of health-oriented behaviour in this regard and the influence of the medical personnel on women's education.ResultsAge is a factor diversifying the knowledge of the breast self-examination method. Doctors and nurses were rarely indicated as a source of knowledge concerning breast cancer prophylaxis. The subjects presented a high level of knowledge of the factors increasing the risk of developing cancer.ConclusionsA correlation between the level of education and the knowledge of one's own breast to a degree which enables a woman to detect even a slight change was observed. Vital findings also concern the sources of knowledge concerning breast cancer prophylaxis. The results of the studies indicated little informative support on the part of the medical personnel; therefore, one should call for supplementing training courses for doctors and nurses focusing on the issues of prophylaxis, including the method of breast self-examination.     

Using Functional Genetics to Understand Breast Cancer Biology

Genetic screens were for long the prerogative of those that studied model organisms. The discovery in 2001 that gene silencing through RNA interference (RNAi) can also be brought about in mammalian cells paved the way for large scale loss-of-function genetic screens in higher organisms. In this article, we describe how functional genetic studies can help us understand the biology of breast cancer, how it can be used to identify novel targets for breast cancer therapy, and how it can help in the identification of those patients that are most likely to respond to a given therapy.Much remains to be learned regarding the function of mammalian genes. Only some quarter of all human genes have well-described functions. It is likely that quite a few of these currently unannotated genes will turn out to play key parts in cancer biology. For example, a 70-gene gene signature that can discriminate breast tumors of good and poor prognosis contained some 20 genes of currently unknown function (van ‘t Veer et al. 2002). The fact that these genes of unknown function foretell breast cancer prognosis hints at a role for at least some of these genes in breast cancer biology. The unbiased search for genes that contribute to breast cancer development is therefore likely to yield a rich harvest of new insights. RNA interference allows us to suppress genes systematically on a large scale and study the effects of gene suppression on specific cellular processes or signaling pathways. Consequently, RNA interference-based genetic screens have the potential to deepen our understanding of the molecular events that cause breast cancer, to find novel targets for therapy and to find biomarkers of drug responsiveness. In this article, we will describe the technologies available to perform both gain-of-function and loss-of-function genetic screens and will illustrate how such functional genetic screens have been used in the recent past to study a variety of outstanding questions in the biology of breast cancer.